File size: 4,334 Bytes
d958fc1 c84502c d958fc1 c84502c d958fc1 c84502c d958fc1 c84502c d958fc1 c84502c d958fc1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import math
import gradio as gr
# ===== DATASET =====
with open("dataset.txt", "r", encoding="utf-8") as f:
text = f.read().lower()
chars = sorted(list(set(text)))
vocab_size = len(chars)
stoi = {ch:i for i,ch in enumerate(chars)}
itos = {i:ch for i,ch in enumerate(chars)}
def encode(s): return [stoi.get(c, 0) for c in s]
def decode(l): return "".join([itos[i] for i in l])
# ===== GPT-Style Transformer Decoder =====
class GPTBlock(nn.Module):
def __init__(self, d_model, nhead, dim_feedforward, dropout):
super().__init__()
self.attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
self.ff = nn.Sequential(
nn.Linear(d_model, dim_feedforward),
nn.GELU(),
nn.Linear(dim_feedforward, d_model),
nn.Dropout(dropout),
)
self.ln1 = nn.LayerNorm(d_model)
self.ln2 = nn.LayerNorm(d_model)
def forward(self, x, mask=None):
attn_out, _ = self.attn(x, x, x, attn_mask=mask)
x = self.ln1(x + attn_out)
ff_out = self.ff(x)
x = self.ln2(x + ff_out)
return x
class GPTModel(nn.Module):
def __init__(self, vocab_size, d_model=128, nhead=8, num_layers=4, dim_feedforward=512, max_len=5000, dropout=0.1):
super().__init__()
self.token_emb = nn.Embedding(vocab_size, d_model)
self.pos_emb = nn.Parameter(torch.zeros(1, max_len, d_model))
self.blocks = nn.ModuleList([GPTBlock(d_model, nhead, dim_feedforward, dropout) for _ in range(num_layers)])
self.ln_f = nn.LayerNorm(d_model)
self.head = nn.Linear(d_model, vocab_size)
def forward(self, x):
seq_len = x.size(1)
token_embeddings = self.token_emb(x) # (batch, seq_len, d_model)
pos_embeddings = self.pos_emb[:, :seq_len, :] # (1, seq_len, d_model)
x = token_embeddings + pos_embeddings
x = x.transpose(0, 1) # for MultiheadAttention: (seq_len, batch, d_model)
# causal mask (upper triangular)
mask = torch.triu(torch.ones(seq_len, seq_len) * float('-inf'), diagonal=1).to(x.device)
for block in self.blocks:
x = block(x, mask)
x = x.transpose(0, 1) # back to (batch, seq_len, d_model)
x = self.ln_f(x)
logits = self.head(x) # (batch, seq_len, vocab_size)
return logits
# ===== TRAINING =====
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = GPTModel(vocab_size).to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=0.005)
criterion = nn.CrossEntropyLoss()
seq_len = 25
batch_size = 1
epochs = 300
data_tensor = torch.tensor(encode(text), dtype=torch.long)
for epoch in range(epochs):
model.train()
idx = np.random.randint(0, len(data_tensor) - seq_len - 1)
chunk = data_tensor[idx:idx+seq_len+1].unsqueeze(0).to(device) # (1, seq_len+1)
input_seq = chunk[:, :-1]
target_seq = chunk[:, 1:]
optimizer.zero_grad()
logits = model(input_seq)
loss = criterion(logits.view(-1, vocab_size), target_seq.view(-1))
loss.backward()
optimizer.step()
if epoch % 50 == 0:
print(f"Epoch {epoch}, Loss: {loss.item():.4f}")
# ===== TEXT GENERATION =====
def generate_text(model, seed, max_len=100):
model.eval()
input_ids = torch.tensor(encode(seed), dtype=torch.long).unsqueeze(0).to(device)
generated = seed
with torch.no_grad():
for _ in range(max_len):
logits = model(input_ids)
probs = F.softmax(logits[0, -1], dim=-1).cpu().numpy()
next_id = np.random.choice(len(probs), p=probs)
generated += itos[next_id]
next_token = torch.tensor([[next_id]], device=device)
input_ids = torch.cat([input_ids, next_token], dim=1)
return generated
# ===== GRADIO CHAT =====
def chat_with_ai(inp):
return generate_text(model, inp, max_len=100)[len(inp):]
import gradio as gr
iface = gr.Interface(fn=chat_with_ai,
inputs=gr.Textbox(lines=1, placeholder="Ketik chat kamu..."),
outputs="text",
title="Chat AI Transformer GPT Style",
description="Chat AI pake model Transformer GPT-style sederhana")
iface.launch()
|